Metadata-Version: 2.1
Name: deepflash2
Version: 0.0.13
Summary: A Deep learning pipeline for segmentation of fluorescent labels in microscopy images
Home-page: https://github.com/matjesg/deepflash2
Author: Matthias Griebel
Author-email: matthias.griebel@uni-wuerzburg.com
License: Apache Software License 2.0
Description: # deepflash2
        > Official repository of DeepFLasH2 - a deep learning pipeline for segmentation of fluorescent labels in microscopy images.
        
        
         ![CI](https://github.com/matjesg/deepflash2/workflows/CI/badge.svg) [![PyPI](https://img.shields.io/pypi/v/deepflash2?color=blue&label=pypi%20version)](https://pypi.org/project/deepflash2/#description) [![Conda (channel only)](https://img.shields.io/conda/vn/matjesg/deepflash2?color=seagreen&label=conda%20version)](https://anaconda.org/matjesg/deepflash2) [![Build fastai images](https://github.com/matjesg/deepflash2/workflows/Build%20deepflash2%20images/badge.svg)](https://github.com/matjesg/deepflash2)
        
        ## Why using deepflash2?
        
        __The best of two worlds:__
        Combining state of the art deep learning with a barrier free environment for life science researchers.
        
        - End-to-end process for life science researchers
            - no coding skills required
            - free usage on _Google Colab_ at no costs
            - easy deployment on own hardware
        - Rigorously evaluated deep learning models
            - Model Library
            - easy integration new (*pytorch*) models
        - Best practices model training
            - leveraging the _fastai_ library
            - mixed precision training
            - learning rate finder and fit one cycle policy 
            - advanced augementation 
        - Reliable prediction on new data
            - leveraging Bayesian Uncertainties
        
        
        
        
        
        ## Workflow
        
        tbd
        
        ## Installing
        
        You can use **deepflash2** by using [Google Colab](colab.research.google.com). You can run every page of the [documentation](matjesg.github.io/deepflash2/) as an interactive notebook - click "Open in Colab" at the top of any page to open it.
         - Be sure to change the Colab runtime to "GPU" to have it run fast!
         - Use Firefox or Google Chrome if you want to upload your images.
        
        You can install **deepflash2**  on your own machines with conda (highly recommended):
        
        ```bash
        conda install -c fastai -c pytorch -c matjesg deepflash2 
        ```
        To install with pip, use
        
        ```bash
        pip install deepflash2
        ```
        If you install with pip, you should install PyTorch first by following the PyTorch [installation instructions](https://pytorch.org/get-started/locally/).
        
        ## Using Docker
        
        Docker images for __deepflash2__ are built on top of [the latest pytorch image](https://hub.docker.com/r/pytorch/pytorch/) and [fastai](https://github.com/fastai/docker-containers) images. **You must install [Nvidia-Docker](https://github.com/NVIDIA/nvidia-docker) to enable gpu compatibility with these containers.**
        
        - CPU only
        > `docker run -p 8888:8888 matjesg/deepflash`
        - With GPU support ([Nvidia-Docker](https://github.com/NVIDIA/nvidia-docker) must be installed.)
        has an editable install of fastai and fastcore.
        > `docker run --gpus all -p 8888:8888 matjesg/deepflash`
        All docker containers are configured to start a jupyter server. **deepflash2** notebooks are available in the `deepflash2_notebooks` folder.
        
        For more information on how to run docker see [docker orientation and setup](https://docs.docker.com/get-started/) and [fastai docker](https://github.com/fastai/docker-containers).
        
        ## Model Library
        
        We provide a model library with pretrained model weights. Visit our [model library documentation](https://matjesg.github.io/deepflash2/model_library.html) for information on the datasets of the pretrained models.
        
        ## Creating segmentation masks with Fiji/ImageJ
        
        If you don't have labelled training data available, you can use this [instruction manual](https://github.com/matjesg/DeepFLaSH/raw/master/ImageJ/create_maps_howto.pdf) for creating segmentation maps.
        The ImagJ-Macro is available [here](https://raw.githubusercontent.com/matjesg/DeepFLaSH/master/ImageJ/Macro_create_maps.ijm).
        
        ## Acronym
        
        A Deep-learning pipeline for Fluorescent Label Segmentation that learns from Human experts
        
Keywords: unet,deep learning,semantic segmentation,microscopy,fluorescent labels
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
